SoroushVahidi/scidocs huggingface dataset:
Tactical driver behavior prediction and intent inference: A review
(2011)

Drawing upon fundamental research in human behavior prediction, recently there has been a research focus on how to predict driver behaviors. In this paper we review the field of driver behavior and intent prediction, with a specific focus on tactical maneuvers, as opposed to operational or strategic maneuvers. The aim of a driver behavior prediction system is to forecast the trajectory of the vehicle prior in real-time, which could allow a Driver Assistance System to compensate for dangerous or uncomfortable circumstances. This review provides insights into the scope of the problem, as well as the inputs, algorithms, performance metrics, and shortcomings in the state-of-the-art systems.
Validity and reliability of the Cohen 10-item Perceived Stress Scale in patients with chronic headache: Persian version.
(2017)

BACKGROUND
The Cohen Perceived Stress Scale is being used widely in various countries. The present study evaluated the validity and reliability of the Cohen 10-item Perceived Stress Scale (PSS-10) in assessing tension headache, migraine, and stress-related diseases in Iran.


METHODS
This study is a methodological and cross-sectional descriptive investigation of 100 patients with chronic headache admitted to the pain clinic of Baqiyatallah Educational and Therapeutic Center. Convenience sampling was used for subject selection. PSS psychometric properties were evaluated in two stages. First, the standard scale was translated. Then, the face validity, content, and construct of the translated version were determined.


RESULTS
The average age of participants was 38 years with a standard deviation (SD) of 13.2. As for stress levels, 12% were within the normal range, 36% had an intermediate level, and 52% had a high level of stress. The face validity and scale content were remarkable, and the KMO coefficient was 0.82. Bartlett's test yielded 0.327 which was statistically significant (p<0.0001) representing the quality of the sample. In factor analysis of the scale, the two elements of "coping" and "distress" were determined. A Cronbach's Alpha coefficient of 0.72 was obtained. This confirmed the remarkable internal consistency and stability of the scale through repeated measure tests (0.93).


CONCLUSION
The Persian PSS-10 has good internal consistency and reliability. The availability of a validated Persian PSS-10 would indicate a link between stress and chronic headache.
Comparison of Architectural Design Decisions for Resource-Constrained Self-Driving Cars - A Multiple Case-Study
(2014)

Context: Self-Driving cars are getting more and more attention with public demonstration from all important automotive OEMs but also from companies, which do not have a long history in the automotive industry. Fostered by large international competitions in the last decade, several automotive OEMs have already announced to bring this technology to the market around 2020. Objective: International competitions like the 2007 DARPA Urban Challenge did not focus on efficient usage of resources to realize the self-driving vehicular functionality. Since the automotive industry is very cost-sensitive, realizing reliable and robust selfdriving functionality is challenging when expensive and sophisticated sensors mounted very visibly on the vehicle’s roof for example cannot be used. Therefore, the goal for this study is to investigate how architectural design decisions of recent self-driving vehicular technology consider resource-efficiency. Method: In a multiple case study, the architectural design decisions derived for resourceconstrained self-driving miniature cars for the international competition CaroloCup are compared with architectural designs from recent real-scale self-driving cars. Results: Scaling down available resources for realizing self-driving vehicular technology puts additional constraints on the architectural design; especially reusability of software components in platform-independent algorithmic concepts are prevailing. Conclusion: Software frameworks like the robotic operating system (ROS) enable fast prototypical solutions; however, architectural support for resource-constrained devices is limited. Here, architectural design drivers as realized in AUTOSAR are more suitable.
Constructive Induction of Cartesian Product Attributes
(1996)

Constructive induction is the process of changing the representation of examples by creating new attributes from existing attributes. In classi cation, the goal of constructive induction is to nd a representation that facilitates learning a concept description by a particular learning system. Typically, the new attributes are Boolean or arithmetic combinations of existing attributes and the learning algorithms used are decision trees or rule learners. We describe the construction of new attributes that are the Cartesian product of existing attributes. We consider the e ects of this operator on a Bayesian classi er an a nearest neighbor algorithm.
Analyzing User Preference for Social Image Recommendation
(2016)

With the incredibly growing amount of multimedia data shared on the social media platforms, recommender systems have become an important necessity to ease users’ burden on the information overload. In such a scenario, extensive amount of heterogeneous information such as tags, image content, in addition to the user-to-item preferences, is extremely valuable for making effective recommendations. In this paper, we explore a novel hybrid algorithm termed STM, for image recommendation. STM jointly considers the problem of image content analysis with the users’ preferences on the basis of sparse representation. STM is able to tackle the challenges of highly sparse user feedbacks and cold-start problmes in the social network scenario. In addition, our model is based on the classical probabilistic matrix factorization and can be easily extended to incorporate other useful information such as the social relationships. We evaluate our approach with a newly collected 0.3 million social image data set from Flickr. The experimental results demonstrate that sparse topic modeling of the image content leads to more effective recommendations, , with a significant performance gain over the state-of-the-art alternatives.
Novel microstrip patch antenna design employing flexible PVC substrate suitable for defence and radio-determination applications
(2016)

This paper propounds a new flexible material, which has been designed by employing flexible Poly Vinyl Chloride (PVC) having dielectric constant εr= 2.7 as substrate. The projected antenna design states that a circular shaped copper patch with flag shaped slot on it has been deployed over the hexagonal PVC substrate of thickness 1 mm. Then ground plane is partially minimized and slotted to revamp the antenna performance. The proposed antenna operates within frequency range of 7.2032GHz to 7.9035GHz while resonating at 7.55GHz. The proposed antenna design has minimal return loss of −55.226dB with the impedance bandwidth of 700 MHz, gain of 4.379dB and directivity of 4.111dBi. The antenna has VSWR less than maximum acceptable value of 2. The projected antenna design can be suitably employed for UWB, radio-determination applications (7.235GHz to 7.25GHz), naval, defence systems (7.25GHz – 7.3GHz) and weather satellite applications (7.75GHz to 7.90GHz). The antenna has been designed in CST Microwave Studio 2014. The proposed antenna has been fabricated and tested using E5071C Network Analyser and anechoic chamber. It has been observed that the stimulated results closely match with the experimental results.
Multi-Type Itemset Embedding for Learning Behavior Success
(2018)

Contextual behavior modeling uses data from multiple contexts to discover patterns for predictive analysis. However, existing behavior prediction models often face difficulties when scaling for massive datasets. In this work, we formulate a behavior as a set of context items of different types (such as decision makers, operators, goals and resources), consider an observable itemset as a behavior success, and propose a novel scalable method, "multi-type itemset embedding", to learn the context items' representations preserving the success structures. Unlike most of existing embedding methods that learn pair-wise proximity from connection between a behavior and one of its items, our method learns item embeddings collectively from interaction among all multi-type items of a behavior, based on which we develop a novel framework, LearnSuc, for (1) predicting the success rate of any set of items and (2) finding complementary items which maximize the probability of success when incorporated into an itemset. Extensive experiments demonstrate both effectiveness and efficency of the proposed framework.
Context augmented Dynamic Bayesian Networks for event recognition
(2014)

This paper proposes a new Probabilistic Graphical Model (PGM) to incorporate the scene, event object interaction, and the event temporal contexts into Dynamic Bayesian Networks (DBNs) for event recognition in surveillance videos. We first construct the baseline event DBNs for modeling the events from their own appearance and kinematic observations, and then augment the DBN with contexts to improve its event recognition performance. Unlike the existing context methods, our model incorporates various contexts simultaneously into one unified model. Experiments on real scene surveillance datasets with complex backgrounds show that the contexts can effectively improve the event recognition performance even under great challenges like large intra-class variations and low image resolution. The topic of modeling and recognizing events in video surveillance system has attracted growing interest from both academia and industry (Oh et al., 2011). Various graphical, syntactic, and description-based approaches (Turaga et al., 2008) have been introduced for modeling and understanding events. Among those approaches, the time-sliced graphical models, i.e. Hidden Markov Models (HMMs) and Dynamic Bayesian Networks (DBNs), have become popular tools. However, surveillance video event recognition still faces difficulties even with the well-built models for describing the events. The first difficulty arises from the tremendous intra-class variations in events. The same category of events can have huge variations in their observations due to visual appearances differences, target motion variations, viewpoint change and temporal variability. Also, the low resolution of event targets also affects event recognition. To compensate for such challenges, we propose to capture various contextual knowledge and systematically integrate them with image data using a Probabilistic Graphical Model (PGM) (Koller and Friedman, 2009) to improve the performance of event recognition on challenging surveillance videos. Contextual knowledge can be regarded as one type of extra information that does not directly describe the recognition task, but it can support the task. As an additional information that can capture certain temporal, spatial or logical relationships with the recognition target, context plays an important role for various visual recognition tasks. Various contexts that are available/retriev-able both during training and testing are widely used in many approaches. For example, Yao and Fei-Fei (2010) and Yao and Fei-Fei (2012) propose a context model to make human pose estimation task and object detection task as mutual context to help each other. Also, Ding et al. (2012) use both the local features and the context cues in the neighborhood windows to construct a combined feature …
Learning from Labeled and Unlabeled Data with LabelPropagationXiaojin
(2002)

We investigate the use of unlabeled data to help labeled data in cl ssification. We propose a simple iterative algorithm, label pro pagation, to propagate labels through the dataset along high density are as d fined by unlabeled data. We analyze the algorithm, show its solution , and its connection to several other algorithms. We also show how to lear n p ameters by minimum spanning tree heuristic and entropy minimiz ation, and the algorithm’s ability to perform feature selection. Expe riment results are promising.
Mobile App Tagging
(2016)

Mobile app tagging aims to assign a list of keywords indicating core functionalities, main contents, key features or concepts of a mobile app. Mobile app tags can be potentially useful for app ecosystem stakeholders or other parties to improve app search, browsing, categorization, and advertising, etc. However, most mainstream app markets, e.g., Google Play, Apple App Store, etc., currently do not explicitly support such tags for apps. To address this problem, we propose a novel auto mobile app tagging framework for annotating a given mobile app automatically, which is based on a search-based annotation paradigm powered by machine learning techniques. Specifically, given a novel query app without tags, our proposed framework (i) first explores online kernel learning techniques to retrieve a set of top-N similar apps that are semantically most similar to the query app from a large app repository; and (ii) then mines the text data of both the query app and the top-N similar apps to discover the most relevant tags for annotating the query app. To evaluate the efficacy of our proposed framework, we conduct an extensive set of experiments on a large real-world dataset crawled from Google Play. The encouraging results demonstrate that our technique is effective and promising.
Robust Lane Detection and Tracking for Real-Time Applications
(2018)

An effective lane-detection algorithm is a fundamental component of an advanced driver assistant system, as it provides important information that supports driving safety. The challenges faced by the lane detection and tracking algorithm include the lack of clarity of lane markings, poor visibility due to bad weather, illumination and light reflection, shadows, and dense road-based instructions. In this paper, a robust and real-time vision-based lane detection algorithm with an efficient region of interest is proposed to reduce the high noise level and the calculation time. The proposed algorithm also processes a gradient cue and a color cue together and a line clustering with scan-line tests to verify the characteristics of the lane markings. It removes any false lane markings and tracks the real lane markings using the accumulated statistical data. The experiment results show that the proposed algorithm gives accurate results and fulfills the real-time operation requirement on embedded systems with low computing power.
Space-time video completion
(2004)

We present a method for space-time completion of large space-time "holes" in video sequences of complex dynamic scenes. The missing portions are filled-in by sampling spatio-temporal patches from the available parts of the video, while enforcing global spatio-temporal consistency between all patches in and around the hole. This is obtained by posing the task of video completion and synthesis as a global optimization problem with a well-defined objective function. The consistent completion of static scene parts simultaneously with dynamic behaviors leads to realistic looking video sequences. Space-time video completion is useful for a variety of tasks, including, but not limited to: (i) Sophisticated video removal (of undesired static or dynamic objects) by completing the appropriate static or dynamic background information, (ii) Correction of missing/corrupted video frames in old movies, and (iii) Synthesis of new video frames to add a visual story, modify it, or generate a new one. Some examples of these are shown in the paper.
Predictors of physical restraint use in Canadian intensive care units
(2014)

INTRODUCTION
Physical restraint (PR) use in the intensive care unit (ICU) has been associated with higher rates of self-extubation and prolonged ICU length of stay. Our objectives were to describe patterns and predictors of PR use.


METHODS
We conducted a secondary analysis of a prospective observational study of analgosedation, antipsychotic, neuromuscular blocker, and PR practices in 51 Canadian ICUs. Data were collected prospectively for all mechanically ventilated adults admitted during a two-week period. We tested for patient, treatment, and hospital characteristics that were associated with PR use and number of days of use, using logistic and Poisson regression respectively.


RESULTS
PR was used on 374 out of 711 (53%) patients, for a mean number of 4.1 (standard deviation (SD) 4.0) days. Treatment characteristics associated with PR were higher daily benzodiazepine dose (odds ratio (OR) 1.05, 95% confidence interval (CI) 1.00 to 1.11), higher daily opioid dose (OR 1.04, 95% CI 1.01 to 1.06), antipsychotic drugs (OR 3.09, 95% CI 1.74 to 5.48), agitation (Sedation-Agitation Scale (SAS) >4) (OR 3.73, 95% CI 1.50 to 9.29), and sedation administration method (continuous and bolus versus bolus only) (OR 3.09, 95% CI 1.74 to 5.48). Hospital characteristics associated with PR indicated patients were less likely to be restrained in ICUs from university-affiliated hospitals (OR 0.32, 95% CI 0.17 to 0.61). Mainly treatment characteristics were associated with more days of PR, including: higher daily benzodiazepine dose (incidence rate ratio (IRR) 1.07, 95% CI 1.01 to 1.13), daily sedation interruption (IRR 3.44, 95% CI 1.48 to 8.10), antipsychotic drugs (IRR 15.67, 95% CI 6.62 to 37.12), SAS <3 (IRR 2.62, 95% CI 1.08 to 6.35), and any adverse event including accidental device removal (IRR 8.27, 95% CI 2.07 to 33.08). Patient characteristics (age, gender, Acute Physiology and Chronic Health Evaluation II score, admission category, prior substance abuse, prior psychotropic medication, pre-existing psychiatric condition or dementia) were not associated with PR use or number of days used.


CONCLUSIONS
PR was used in half of the patients in these 51 ICUs. Treatment characteristics predominantly predicted PR use, as opposed to patient or hospital/ICU characteristics. Use of sedative, analgesic, and antipsychotic drugs, agitation, heavy sedation, and occurrence of an adverse event predicted PR use or number of days used.
Reference scope identification for citances by classification with text similarity measures
(2017)

This paper targets at the first step towards generating citation summaries - to identify the reference scope (i.e., cited text spans) for citances. We present a novel classification-based method that converts the task into binary classification which distinguishes cited and non-cited pairs of citances and reference sentences. The method models pairs of citances and reference sentences as feature vectors where citation-dependent and citation-independent features based on the semantic similarity between texts and the significance of texts are explored. Such vector representations are utilized to train a binary classifier. For a citance, once the set of reference sentences classified as the cited sentences are collected, a heuristic-based filtering strategy is applied to refine the output. The method is evaluated using the CL-SciSumm 2016 datasets and found to perform well with competitive results.
The Role of Context for Object Detection and Semantic Segmentation in the Wild
(2014)

In this paper we study the role of context in existing state-of-the-art detection and segmentation approaches. Towards this goal, we label every pixel of PASCAL VOC 2010 detection challenge with a semantic category. We believe this data will provide plenty of challenges to the community, as it contains 520 additional classes for semantic segmentation and object detection. Our analysis shows that nearest neighbor based approaches perform poorly on semantic segmentation of contextual classes, showing the variability of PASCAL imagery. Furthermore, improvements of existing contextual models for detection is rather modest. In order to push forward the performance in this difficult scenario, we propose a novel deformable part-based model, which exploits both local context around each candidate detection as well as global context at the level of the scene. We show that this contextual reasoning significantly helps in detecting objects at all scales.
Microsoft COCO: Common Objects in Context
(2014)

We present a new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding. This is achieved by gathering images of complex everyday scenes containing common objects in their natural context. Objects are labeled using per-instance segmentations to aid in precise object localization. Our dataset contains photos of 91 objects types that would be easily recognizable by a 4 year old. With a total of 2.5 million labeled instances in 328k images, the creation of our dataset drew upon extensive crowd worker involvement via novel user interfaces for category detection, instance spotting and instance segmentation. We present a detailed statistical analysis of the dataset in comparison to PASCAL, ImageNet, and SUN. Finally, we provide baseline performance analysis for bounding box and segmentation detection results using a Deformable Parts Model.
Areas of Attention for Image Captioning
(2017)

We propose “Areas of Attention”, a novel attentionbased model for automatic image captioning. Our approach models the dependencies between image regions, caption words, and the state of an RNN language model, using three pairwise interactions. In contrast to previous attentionbased approaches that associate image regions only to the RNN state, our method allows a direct association between caption words and image regions. During training these associations are inferred from image-level captions, akin to weakly-supervised object detector training. These associations help to improve captioning by localizing the corresponding regions during testing. We also propose and compare different ways of generating attention areas: CNN activation grids, object proposals, and spatial transformers nets applied in a convolutional fashion. Spatial transformers give the best results. They allow for image specific attention areas, and can be trained jointly with the rest of the network. Our attention mechanism and spatial transformer attention areas together yield state-of-the-art results on the MSCOCO dataset.
Multi objective outbound logistics network design for a manufacturing supply chain
(2013)

Outbound logistics network (OLN) in the downstream supply chain of a firm plays a dominant role in the success or failure of that firm. This paper proposes the design of a hybrid and flexible OLN in multi objective context. The proposed distribution network for a manufacturing supply chain consists of a set of customer zones (CZs) at known locations with known demands being served by a set of potential manufacturing plants, a set of potential central distribution centers (CDCs), and a set of potential regional distribution centers (RDCs). Three variants of a single product classified based on nature of demand are supplied to CZs through three different distribution channels. The decision variables include number of plants, CDCs, RDCs, and quantities of each variant of product delivered to CZs through a designated distribution channel. The goal is to design the network with multiple objectives so as to minimize the total cost, maximize the unit fill rates, and maximize the resource utilization of the facilities in the network. The problem is formulated as a mixed integer linear programming problem and a multiobjective genetic algorithm (MOGA) called non-dominated sorting genetic algorithm—II (NSGA-II) is employed to solve the resulting NP-hard combinatorial optimization problem. Computational experiments conducted on randomly generated data sets are presented and analyzed showing the effectiveness of the solution algorithm for the proposed network. N. C. Hiremath · S. Sahu · M. K. Tiwari (B) Department of Industrial Engineering and Management, Indian Institute of Technology Kharagpur, Kharagpur, 721302 West Bengal, India e-mail: mkt09@hotmail.com N. C. Hiremath e-mail: nchiremath@gmail.com S. Sahu e-mail: sahus@mech.iitkgp.ernet.in